Hypnotic line art is a modern form in which white narrow curved ribbons, with the width and direction varying along each path over a black background, provide a keen sense of 3D objects regarding surface shapes and topological contours. However, the procedure of manually creating such line art work can be quite tedious and time-consuming. In this paper, we present an interactive system that offers a What-You-See-Is-What-You-Get (WYSIWYG) scheme for producing hypnotic line art images by integrating and placing evenly-spaced streamlines in tensor fields. With an input picture segmented, the user just needs to sketch a few illustrative strokes to guide the construction of a tensor field for each part of the objects therein. Specifically, we propose a new method which controls, with great precision, the aesthetic layout and artistic drawing of an array of streamlines in each tensor field to emulate the style of hypnotic line art. Given several parameters for streamlines such as density, thickness, and sharpness, our system is capable of generating professional-level hypnotic line art work. With great ease of use, it allows art designers to explore a wide variety of possibilities to obtain hypnotic line art results of their own preferences.Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022 Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs, create sedimentary logs and combine them in 'correlation panels' to map the extents of key geological horizons, and build a stratigraphic model to understand their position in the ancient landscape. Currently, the creation of correlation panels is completely manual and therefore time-consuming, and inflexible. With InCorr we present a visualization solution that encompasses a 3D logging tool and an interactive data-driven correlation panel that evolves with the stratigraphic analysis. For the creation of InCorr we closely cooperated with leading planetary geologists in the form of a design study. We verify our results by recreating an existing correlation analysis with InCorr and validate our correlation panel against a manually created illustration. Further, we conducted a user-study with a wider circle of geologists. Our evaluation shows that InCorr efficiently supports the domain experts in tackling their research questions and that it has the potential to significantly impact how geologists work with digital outcrop representations in general.Convolutional Neural Networks (CNNs) have emerged as a powerful tool for object detection in 2D images. However, their power has not been fully realised for detecting 3D objects directly in point clouds without conversion to regular grids. Moreover, existing state-of-the-art 3D object detection methods aim to recognize objects individually without exploiting their relationships during learning or inference. In this article, we first propose a strategy that associates the predictions of direction vectors with pseudo geometric centers, leading to a win-win solution for 3D bounding box candidates regression. Secondly, we propose point attention pooling to extract uniform appearance features for each 3D object proposal, benefiting from the learned direction features, semantic features and spatial coordinates of the object points. Finally, the appearance features are used together with the position features to build 3D object-object relationship graphs for all proposals to model their co-existence. We explore the effect of relation graphs on proposals' appearance feature enhancement under supervised and unsupervised settings. The proposed relation graph network comprises a 3D object proposal generation module and a 3D relation module, making it an end-to-end trainable network for detecting 3D objects in point clouds. Experiments on challenging benchmark point cloud datasets (SunRGB-D, ScanNet and KITTI) show that our algorithm performs better than existing state-of-the-art.Detection and counting of biological living cells in continuous fluidic flows play an essential role in many applications for early diagnosis and treatment of diseases. In this regard, this study highlighted the proposal of a biochip system for detecting and enumerating human lung carcinoma cell flow in the microfluidic channel. The principle of detection was based on the change of impedance between sensing electrodes integrated in the fluidic channel, due to the presence of a biological cell in the sensing region. A compact electronic module was built to sense the unbalanced impedance between the sensing microelectrodes. It consisted of an instrumentation amplifier stage to obtain the difference between the acquired signals, and a lock-in amplifier stage to demodulate the signals at the stimulating frequency as well as to reject noise at other frequencies. The performance of the proposed system was validated through experiments of A549 cells detection as they passed over the microfluidic channel. The experimental results indicated the occurrence of large spikes (up to approximately 180 mV) over the background signal according to the passage of a single A549 cell in the continuous flow. The proposed device is simple-to-operate, inexpensive, portable, and exhibits high sensitivity, which are suitable considerations for developing point-of-care applications.Capturing the interactions of human articulations lies in the center of skeleton-based action recognition. https://www.selleckchem.com/ALK.html Recent graph-based methods are inherently limited in the weak spatial context modeling capability due to fixed interaction pattern and inflexible shared weights of GCN. To address above problems, we propose the Multi-View Interactional Graph Network (MV-IGNet) which can construct, learn and infer multi-level spatial skeleton context, including view-level (global), group-level, joint-level (local) context, in a unified way. MV-IGNet leverages different skeleton topologies as multi-views to cooperatively generate complementary action features. For each view, Separable Parametric Graph Convolution (SPG-Conv) enables multiple parameterized graphs to enrich local interaction patterns, which provides strong graph-adaption ability to handle irregular skeleton topologies. We also partition the skeleton into several groups and then the higher-level group contexts including inter-group and intra-group, are hierarchically captured by above SPG-Conv layers.